How to Preprocessing Time Series Data for Supervised Machine Learning

Hi All
I want to develop a Machine Learning model for the prediction of maintenance.

I have the Failure data which is related to the
( Failure date, failure time, Recovery Date, Recovery time, etc…),
(Exa 05/10/2022- 11:00 Failure - Recovery 05/10/2022- 11:30
08/10/2022 13:00 Failure Recovery 08/10/2022- 15:10)

What Models can be used to convert the (Date and Time data) to be used for the machine learning algorithm for the predictive model?
in the other worlds
How to convert this data to be readable for the ML model

Thanks in advance

Hello Ahmad,

Can you give detail about what kind of data is this about??

is this model prediction about any product related or event related data??

You can actually use the supervised learning algorithm but I need to know more about the data, your detail is very brief or vague to understand about what you have and what you want to do.

In case it is a primary issue, you can DM me the details about the above question via DM.


1 Like

The normal method for dealing with dates and times would be to convert them to a single number that is the number of seconds or microseconds from some fixed point in the past, e.g. January 1, 1970 00:00:00 UTC. That representation is referred to as “POSIX timestamps”. Then you can do arithmetic with the dates, e.g. computing time gaps and the like. There are lots of python functions for dealing with dates and times. Here’s one set of functions to start with. You can also google “POSIX timestamps python” for more information.

1 Like

Thank you Deepti
I have DM

Thanks Paulinpaloalto
for your time and consideration